Computer-Aided Detection (CAD) of colon polyps and lung nodules is an important technology in the early diagnosis of colon cancer and lung cancer. CAD promises to aid physicians in their examination of Virtual Colonoscopy (VC) data for polyps, reducing the possibility of overlooked malignancies. VC is a non-invasive procedure that combines spiral computed tomography or magnetic resonance image data acquisition of an air-filled and cleansed colon with three dimensional imaging software to create virtual endoscopic images of the colonic surface. VC offers a more comfortable screening method for patients, but requires significant time and effort by radiologists to examine large datasets resulting from the high resolution three dimensional images obtained from CT or MR machines. At the same time, these high resolution images offer the potential to detect smaller polyps.
Most Computer Aided Detection (CAD) systems can be divided into four phases: segmentation, candidate generation, feature collection and classification. Identifying polyps using CAD is difficult because the polyps are of various shapes and sizes, and because thickening folds and retained stool may mimic their shape and density. In order to detect candidate polyps, some detection techniques create a “response image” that indicates the likelihood that each point in the volume or on the colon surface is a polyp. In current CAD methods, many false-positive polyp candidates are detected in early stages of the algorithm that must be eliminated in later stages via discriminating features. Most methods make use of features collected from the original CT data.
In current CAD methods, false positives that are created by the initial stages of an algorithm may be eliminated in later stages by collecting and analyzing specific features of each polyp candidate. In order to collect these features, some notion of the space occupied by the candidate must be utilized.
A point location alone, such as the detection point, will only permit a limited number of features to be collected. Some kind of estimate of the volume occupied by a colon polyp or lung nodule must exist to properly collect additional features. Typically, a large number of candidates are detected in the early phases of detection. Any feature collection method must operate quickly to process these candidates.